of 42
Neurophysiological mechanisms of error monitoring in human
and non-human primates
Zhongzheng Fu
1,2
,
Amirsaman Sajad
3,4,5
,
Steven P. Errington
3,4,5
,
Jeffrey D. Schall
3,5,6,7,8
,
Ueli Rutishauser
1,9,10
1
Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
2
Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA,
USA.
3
Center for Integrative & Cognitive Neuroscience, Vanderbilt University, Nashville, TN, USA.
4
Vanderbilt Vision Research Center, Vanderbilt University, Nashville, TN, USA.
5
Department of Psychology, Vanderbilt University, Nashville, TN, USA.
6
Centre for Vision Research, York University, Toronto, Ontario, Canada.
7
Vision: Science to Applications (VISTA), York University, Toronto, Ontario, Canada.
8
Department of Biology, Faculty of Science, York University, Toronto, Ontario, Canada.
9
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA,
USA.
10
Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai
Medical Center, Los Angeles, CA, USA.
Abstract
Performance monitoring is an important executive function that allows us to gain insight into
our own behaviour. This remarkable ability relies on the frontal cortex, and its impairment is an
aspect of many psychiatric diseases. In recent years, recordings from the macaque and human
medial frontal cortex have offered a detailed understanding of the neurophysiological substrate
that underlies performance monitoring. Here we review the discovery of single-neuron correlates
of error monitoring, a key aspect of performance monitoring, in both species. These neurons are
the generators of the error-related negativity, which is a non-invasive biomarker that indexes error
detection. We evaluate a set of tasks that allows the synergistic elucidation of the mechanisms
of cognitive control across the two species, consider differences in brain anatomy and testing
Reprints and permissions information is available at
www.nature.com/reprints
.
Correspondence
should be addressed to Zhongzheng Fu, Jeffrey D. Schall or Ueli Rutishauser. zzbrooksfu@gmail.com;
schalljd@yorku.edu; rutishauseru@csmc.edu.
Author contributions
The authors all contributed to all aspects of preparing the article.
Competing interests
The authors declare no competing interests.
Supplementary information
The online version contains supplementary material available at
https://doi.org/10.1038/
s41583-022-00670-w
.
HHS Public Access
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conditions across species, and describe the clinical relevance of these findings for understanding
psychopathology. Last, we integrate the body of experimental facts into a theoretical framework
that offers a new perspective on how error signals are computed in both species and makes novel,
testable predictions.
Introduction
To survive, all sentient species must adapt to errors
1
. We can, for example, notice that
we pressed a wrong keyboard key without external feedback
2
, or quickly realize that we
have made the wrong turn on our way home or have called an acquaintance by the wrong
name. The ability to recognize action errors is essential for learning new tasks, adapting to
challenging or changing conditions, and interrupting useless or dangerous courses of action
3
.
Error monitoring is the cognitive process by which we rapidly detect situations in which
performance deviates from the intended goal. Error monitoring is an element of performance
monitoring, which is a critical aspect of cognitive control whereby we achieve our goals
4
.
What are the neural processes that endow us with the ability to detect whether we have made
an error and thereby to gain insight into our own behaviour?
It has long been recognized that performance monitoring is critical for goal-directed
behaviour and cognitive control more broadly
4
. In addition, it is increasingly being
appreciated that malfunctioning performance monitoring is a key symptom of some
psychiatric disorders, including impulsive behaviour, obsessive–compulsive disorder,
addiction and schizophrenia
5
,
6
. Hence, performance monitoring is a central construct in
the Research Domain Criteria framework of the US National Institute of Mental Health
,
8
and
a core element in new computational psychiatry-based approaches to mental health
9
. As a
result, deciphering the neural mechanisms that underlie performance monitoring has become
a major interest in both cognitive neuroscience and clinical neuroscience.
The validity of animal model paradigms for higher-level human cognitive processes – in
particular, executive functions such as performance monitoring – remains uncertain
10
,
11
.
Indeed, although much has been learned about the neural mechanisms of cognitive control
in animal models (particularly in the macaque), little is known about the relevance of
these findings for understanding human cognition and its disruption in psychiatric disorders.
Intracranial recordings in humans provide a rare opportunity to directly compare findings in
animal models with those in humans. However, such comparisons are possible only if the
animal model system closely mirrors human anatomy, physiology and behaviour.
The aim of this Review is to compare the neuronal mechanisms for self-monitoring of
action errors across macaques and humans. Recent work involving similar tasks and
assessment of neural activity at the single-neuron level in the medial frontal cortex
(MFC) has demonstrated that there are many similarities in error-monitoring processes
in humans
12
and macaques
13
. At the same time, experiments possible only in humans
reveal the relevance of these processes for cognitive control involved in language, cognitive
flexibility and neuropsychiatric disorders. These studies provide converging evidence that
error neurons in the MFC constitute single-neuron correlates of the error-related negativity
(ERN), an important event-related potential that has spawned a large literature in cognitive
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neuroscience (see later). Informed by these new findings, we synthesize a model for how
errors are computed and propose a set of tasks that are most suitable for multispecies
investigation. We conclude by proposing that the neural processes in the MFC can be studied
synergistically by combining work in humans and macaques to reveal new insights into the
neural mechanisms of cognitive control and its impairment by disease.
Medial frontal cortex
Converging evidence from neuroimaging, electrophysiological recordings and lesion studies
in humans and non-human primates indicates that the MFC is essential for performance
monitoring
4
,
12
-
15
. Two medial frontal regions in particular have been intensely studied
(Fig.1): the first is the supplementary motor complex, which is located within the superior
frontal gyrus (SFG), anterior to the motor cortex. This complex includes the supplementary
motor area (SMA), the pre-supplementary motor area (pre-SMA)
16
and the supplementary
eye field (SEF)
13
,
17
. The second is a collection of areas in both banks of the cingulate sulcus
and the cingulate gyrus. Habitual descriptions of the ‘anterior cingulate cortex’ (ACC),
including ours
12
,
18
,
19
, can be replaced by more precise nomenclature reflecting more refined
anatomical and comparative studies, which distinguish the middle cingulate cortex (MCC)
ventral to the supplementary motor cortex from the ACC around the genu of the corpus
callosum
20
-
22
. The rostral ACC contributes more to emotion and autonomic regulation,
whereas the MCC contributes more to performance monitoring and cognitive control
23
, and
includes the cingulate motor areas that project to the spinal cord
24
.
One of the most robust findings in the MFC in humans and macaques is its vigorous
single-neuron spiking response following erroneous actions
12
,
13
,
19
,
25
-
28
. This signal is also
detectable with non-invasive methods such as functional MRI (fMRI)
4
and simultaneous
fMRI and scalp electroencephalography (EEG)
29
,
30
in humans. Error-monitoring signals
in both the MCC and the SFG are often but not always associated with performance
adjustments, such as post-error slowing
12
,
13
. These findings bridge monitoring processes
and the engagement of subsequent cognitive control. Together with evidence from lesion and
electrical stimulation studies
31
-
33
, this body of research indicates that recording from and
manipulating the MFC provides a powerful paradigm to probe the neural substrate of error
monitoring.
Invasive single-neuron recordings in the MFC can be obtained in both macaques and
humans. In macaques, recordings in the MCC, SEF and SMA, and pre-SMA are routinely
performed with microelectrodes or silicon probes (Fig. 1a). In humans, recordings in the
MFC have been performed in two clinical scenarios (Fig. 1a): first, in patients with epilepsy,
areas along the medial wall of the frontal lobe (including the MCC, SMA and pre-SMA)
are targeted with depth electrodes to localize focal seizure onset zones or seizure spread
patterns
34
,
35
; second, in patients undergoing awake brain surgery for implantation of a
deep brain stimulator or targeted resection, the MFC is targeted using microelectrodes
36
,
37
.
These neurosurgical scenarios provide opportunities to study the contribution of the MFC
to performance monitoring in both macaques and humans with very similar experimental
techniques. The aspects of performance monitoring best suited for study in each species
differ owing to behavioural and technical constraints, with some aspects approachable only
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in humans and others approachable only in macaques (Table 1). We note that although
our focus here is on the role of the SFG and MCC in error monitoring, these brain
areas contribute importantly to other cognitive functions – such as signalling response
conflict
14
,
17
,
38
-
40
– that are beyond the scope of this Review.
The ERN
The MFC has long been thought to be the source of the ERN (also known as the Ne),
which is a brief period of greater negative polarization in the electroencephalogram over
the MFC when an error is made relative to correct trial performance
41
-
46
(Figs. 1b and
2). Although error monitoring has also been investigated with fMRI, the high temporal
resolution of EEG offers unique leverage in the investigation of cognitive control processes.
Furthermore, the ERN can be measured with a single scalp electrode, thereby providing high
translational potential. Consequently, understanding the neural processes that give rise to the
ERN has relevance for studying normal human behaviour and as a potential endophenotype
for psychiatric disease.
Although the ERN has become a major workhorse in cognitive neuroscience, until recently
little was known about the underlying mechanisms that give rise to it. The ERN is
different from sensory-evoked potentials or movement-related potentials that are associated
with observable events. Rather, the ERN indexes an internal state that can be inferred
only indirectly. Therefore, the improved understanding of the ERN that is now emerging
(discussed later) can also provide insights into other event-related potentials, such as the
contingent negative variation and the readiness potential. The ERN is closely related to the
feedback-related negativity (FRN), which is an event-related potential evoked by externally
signalled failures (for example, after a correct response receiving less-than-expected reward
or sensory feedback indicating an error). The two signals have overlapping scalp voltage
distributions
47
, which has led to the hypothesis that both index the computation of prediction
errors
48
. We discuss the validity of this claim further later. We refer to the terms ‘ERN’ and
‘FRN’ whenever the EEG data are analysed time-locked to the erroneous response onset or
feedback onset, respectively. We note that in some studies the term ‘feedback ERN’ is used
instead of ‘FRN’ (for example, see ref.
49
), which we avoid for clarity.
Understanding how the ERN is generated begins with knowing where it arises. In 1994,
using source modelling, researchers showed that a single dipole in the MCC could account
for the spatial distribution of the ERN
50
. Confidence in this conclusion cannot be high,
though, because the inverse problem of locating dipoles given a spatial distribution of
voltages has no unique solution (Fig. 1b). More uncertainty arises because the simplified
geometric models of the head that were used in the 1990s were poor approximations of
the human brain and head. Another uncertainty overlooked by all early and most current
studies arises from variation in sulcal morphology across individuals
51
, which necessarily
changes the orientation of dipoles (Fig. 1b). Of most relevance for the ERN is the absence
or presence of a paracinguiate sulcus (PCS), which is located superior to the cingulate sulcus
in ~70% of humans
52
,
53
. Such morphological differences in cortical folding patterns produce
differences in EEG voltage distributions; for example, a weaker, briefer N400 versus a
stronger, longer N400 when response conflict is detected
54
.
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Although the inverse problem of source localization of current locations from voltages is
ill-posed and offers indefinite results, forward modelling from current locations to voltage
distributions offers definite results. Recent work using this approach has demonstrated that
a spatial pattern of voltage resembling the ERN can be produced by a simulated dipole
in the SEF of macaques
55
. The biophysics underlying the relationships between neuronal
spiking, synaptic potentials and EEG signals has many complexities. For example, the
relationship between current dipoles in the cerebral cortex and surface EEG signals has
most commonly been described only in terms of instantaneous electric fields. However,
research has demonstrated that the slower diffusion of ions, possibly mediated by glia
56
, can
interact with current dipoles in producing the EEG signal. Relative to granular sensory areas,
agranular cortical areas have a higher ratio of glial cells to neurons
57
, so the contribution of
slower ion diffusion to the EEG signal can differ between granular and agranular areas. To
our knowledge this has not been investigated.
If both the SFG and the MCC separately signal errors, then the ERN can have at least
two sources
12
. Moreover, if error-related signals are produced in different areas within
the MCC, then the number of sources increases further. The voltage measured with an
electrode on the head will be the superposition of the voltages produced by current dipoles
in each cortical area. The specific character of that superposition depends on the geometry
of the cortex, which specifies dipole location, orientation and distance relative to surface
electrodes. Voltages from two sources can sum or cancel depending on the orientation of
the dipoles. This means that the ERN is a manifestation of multiple neural signals arising
in different cortical areas
58
(Fig. 1b). Given the similar anatomical location of the pre-SMA
in humans and macaques, its biophysical contribution to the ERN sampled at the midline
with EEG electrodes is expected to be similar in both species (Fig. 1b). However, we expect
that dipoles established in the SEF of macaques will contribute more to the ERN than
those in the SEF of humans (Fig. 1b). Previous comparisons of human and macaque MFC
functions
59
have not incorporated these details of cortical structure.
Spectral decomposition of the EEG signal has provided additional insights into the processes
generating the ERN. For example, changes in theta-band and delta-band oscillations along
the frontal midline coincide with the ERN
60
-
62
, with both phase resetting and changes
of power in ongoing theta oscillations contributing to this signal
63
,
64
. More broadly, theta-
band activity in the MFC has been described as a mesoscale neural correlate of cognitive
control
65
. Although they provide powerful insights, these data have not revealed the specific
contributions of different parts of the MFC to the ERN. They also do not provide a cellular-
level explanation of how the changes in power and phase of theta-band EEG arise following
the commission of errors.
In humans, detection of errors sometimes evokes error awareness, which is a metacognitive
reflection that enables conscious reasoning about errors. The relationship between error
awareness and the ERN, and the positive polarization that follows the ERN (known as the
Pe component), is complex and remains an active area of research
66
,
67
. For our purposes
it suffices to note that although humans are typically aware of the kind of errors that are
accompanied by an ERN in standard tasks, this does not appear to be necessary; an ERN
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can still be present for unaware errors
68
. We therefore posit that the mechanisms of error
detection we describe are not conditional on awareness.
Intracranial recordings across species
A key goal of this Review is to contrast findings across species (Figs. 2 and 3). Single
neurons that signal errors have been found in the SEF
13
, pre-SMA, SMA
16
and MCC
19
in non-human primates and in the pre-SMA and MCC in humans
12
(Fig. 2a,b). The
temporal properties of these neurons are similar across species (Fig. 3c,d): the spike rate
is maximally modulated ~100 ms after an erroneous action (saccade, arm movement or
finger button press). Two features determine that the error signal reported in these studies
is not exclusively of sensory origin. First, the timing of error signals is synchronized with
the muscle contraction rather than any sensory events. Second, the error signal arises before
the trial outcome is signalled by external feedback about accuracy or the gain or loss of a
reward. Also, the magnitude of single-neuron error signals depends weakly, if at all, on the
laterality of the motor effector
19
,
69
. It is unknown whether the error-related modulation of
single neurons is invariant across motor effectors. One investigation of the topography of the
ERN observed after errors committed with the hand or foot indicated a common source
70
,
but a more recent study of errors committed with the eyes or the hand and using more
sophisticated modelling of current sources described different sources
71
.
The scalp-recorded ERN reliably occurs ~100 ms after a self-monitored error in macaques
and humans (Fig. 2c,d), despite differences across individuals, task requirements and
effectors (eye, hand, foot or vocal)
46
,
70
-
72
. The intracranial ERN (iERN) also has a similar
peak onset latency between humans and macaques: ~100 ms in the macaque SEF
25
and
~130 ms in the macaque MCC after an oculomotor response
26
; ~100 ms in the human
pre-SMA and ~130 ms in the human MCC after a button press using a finger
12
. In both
species, the iERN and the scalp ERN can be estimated reliably on single trials, with the
iERN having the highest reliability (Fig. 3a,b). Two separate human studies have found,
on a trial-by-trial basis, that the latency and the amplitude of the iERN in the MCC are
correlated with those of the iERN in the pre-SMA
12
or the SMA
35
. In addition, the MCC
iERN occurs only when there is a preceding SMA iERN in sessions where both areas are
recorded simultaneously
35
. This is yet to be confirmed in macaques. These results strongly
suggest a hierarchical relationship between the MCC and the SFG in error processing across
species (Fig. 3c,d), with action errors being first detected in the SFG and then communicated
to the MCC for updating control forward models (see later)
12
,
35
.
In both macaques and humans, two types of error-related neurons have been identified. In
the human MFC, ‘type I’ neurons produce more spikes on average on trials in which an error
was made (‘error trials’) than on trials with a correct response (‘correct trials’), whereas
‘type II’ neurons do the opposite. The proportion of type II error neurons is notably larger
in the pre-SMA (40%) than in the MCC (26%)
12
. A similar distinction has been made in
the macaque SFG, with neurons responding more to error trials than to correct trials being
referred to as ‘error cells’ and those with reduced activity in error trials being referred to
as ‘reward expectation cells’
16
. Note that in the human study, no reward but delayed visual
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feedback on accuracy was provided, suggesting that type II responses cannot be solely
attributed to reward expectation.
Given that the latency of error responses in MFC neurons falls in the same range as that of
the scalp ERN and iERN, a key unresolved question is how these two signals are related.
This question is of particular importance because little is known about how the ERN is
generated even though many computational aspects of this signal are well known
42
. Our
macaque and human data
12
,
13
provide insight into the relation between simultaneously
recorded scalp ERN in macaques or iERN in humans and the activity of error neurons. In
macaques, simultaneous recording of the ERN and the spiking activity of neurons across all
layers of the SEF demonstrates clear associations between the microlevel and macrolevel
error signals
13
. Notably, error neurons in layer 2/3 but not those in layer 5/6 demonstrate
this relationship. In humans, the firing rate of error neurons is predicted by the amplitude
of the iERN in the pre-SMA and the MCC
12
. In both species, this relationship exists
exclusively for error neurons, suggesting that it is not merely a generic biophysical one but is
specific to error computation. Laminar recordings in the human MCC
73
and in the macaque
SEF
13
demonstrate prominent current sinks in layers 2 and 3 during errors, where pyramidal
neurons in lower layer 3 and layer 5 extend their dendrites.
One hypothesis for these cross-species findings is that the ERN reflects the highly
synchronous and aligned electric dipoles generated within error neurons
48
. In our view,
these dipoles are generated by synaptic inputs from the thalamocortical projections and
passive return currents at apical locations, as well as inhibitory actions by interneurons and
projections from top-down regions. The larger the ERN amplitude at a given location is, the
stronger and more synchronous these synaptic inputs may be, and the more effectively these
synaptic inputs can drive the firing of error neurons. These results provide the most direct
evidence to date that the ERN is jointly generated in the SFG and the MCC in primates (Fig.
1b), thereby providing a solid physiological foundation for the many EEG studies that use
this signal to study cognitive control.
A second signal of relevance for error monitoring is response conflict
74
,
75
. We differentiate
between two types of response conflict-related signals: those occurring during action
selection while multiple response options are being considered (ex ante), and those
occurring after a response has been made (ex post). Whereas the former calls for online
control to resolve conflict proactively, the latter is an ‘after the fact’ evaluative signal.
In humans, neurons signalling response conflict ex ante exist in both the MCC and the
pre-SMA
18
,
36
,
76
. In macaques, no ex ante conflict neurons have been found using the
stop-signal task or related conflict tasks, which involve conflict between concurrent go and
stop/distractor processes (see below)
19
,
69
,
77
-
79
. In a task involving visually guided saccades
in the presence of a salient distractor, some have interpreted neural spiking in the macaque
MCC as ex ante response conflict between the goal-compatible and distractor-driven saccade
plans
80
, but this conclusion is debatable
81
. The existence of ex ante conflict signals may
depend on the task used to probe it. Whether neurons in the human MFC signal ex ante
conflict in stop-signal tasks remains an open question. By contrast, neurons signalling ex
post conflict exist commonly in both species
18
,
69
,
77
,
78
. In humans, a subset of neurons in
the pre-SMA and the MCC differentiate between whether a correctly performed action was
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made during high or low conflict, whereas in macaques this signal arises after successfully
cancelling a saccade or an arm movement
69
,
77
,
78
. In humans, ex ante and ex post conflicts
are signalled by different neurons, and multivariate population firing rate patterns do not
generalize across these two periods, suggesting that they represent different types of conflict
signals
18
. Although it may seem puzzling why conflict would be signalled ex post, this
signal plays a critical role in the model we develop later herein.
Last, we consider how the findings discussed differ from those derived from fMRI and/or
scalp EEG. Neither spectral analysis nor fMRI blood oxygenation level-dependent (BOLD)-
based analysis has been able to identify that ex ante and ex post conflict signals are
distinct because both involve temporal smoothing. Therefore, studies on the relationship
between BOLD-based conflict signals and subsequent behavioural adjustments may need to
be revised to clarify whether the effect is due to ex ante or ex post conflict signals. Similarly,
the insight that errors and ex ante conflict are represented by distinct groups of neurons is
uniquely provided by single-neuron studies
12
.
Anatomical differences across species
The foregoing comparison between humans and macaques indicates that the error-
monitoring system is evolutionarily conserved across primates. At the same time, there
are notable differences in the mechanisms underlying such monitoring between humans and
macaques. Investigation of these differences can reveal new insights into the evolution of
cognitive functions.
The human MFC and the macaque MFC exhibit many similar features (Fig. 1). The
agranular areas seem largely homologous in organization and function but differ in their
location on the cortical surface
82
,
83
. In both humans and macaques, the SMA is located
immediately rostral to the primary motor cortex, and the pre-SMA is located rostral to the
SMA (Fig. 1). In both species, the pre-SMA is found on the medial surface. However,
whereas much of the SMA and all of the SEF are located on the dorsal convexity in
macaques
84
, in humans, they are located on the medial wall, with the SEF centred at the
paracentral sulcus
85
.
The folding pattern of sulci in the human MFC is considerably more variable than it is
in the macaque MFC. A notable species difference is the PCS
86
. The PCS is unique to
humans and other apes
52
,
87
but is not present in macaques
82
. As noted earlier herein,
although all humans have a cingulate sulcus, ~70% of humans also have a PCS (most
commonly in the left hemisphere but not the right hemisphere)
52
,
53
. The PCS is also more
prominent in males
51
. To our knowledge, no invasive studies have specifically compared the
neural activity within versus outside the paracingulate gyrus during performance-monitoring
tasks. The PCS is important for understanding performance monitoring for the following
reasons. First, its presence dictates where specific neural signals are located
86
, thereby
influencing what the ERN measures (Fig. 1b). Second, the presence and morphology of
the PCS are correlated with interindividual differences in metacognitive abilities such as
reality monitoring
88
,
89
or the ability to perform tasks with response conflict
90
. Third,
properties of the paracingulate gyrus differ in individuals with psychiatric disorders that
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impair performance monitoring, in particular schizophrenia
91
and obsessive–compulsive
disorder
92
.
A final challenge in translating findings between macaques and humans involves the
intrinsic composition of the cortical tissue. For example, humans, but not macaques, have
large spindle neurons in layer 5 of the cingulate cortex
93
. However, what difference this
makes functionally and for the properties of the local field potential remains unknown.
Furthermore, in both species, relatively little is known about how the functional organization
of the cingulate sulcus contributes to performance monitoring
40
. In macaques, the MCC
comprises several separate anatomically distinct areas, extending ventrally from the dorsal
and ventral banks of the anterior cingulate sulcus, along the medial wall, to the corpus
collosum
94
. Anatomical descriptions of the MCC in macaques note that the cytoarchitecture
of the dorsal and ventral banks of the cingulate sulcus differ. Whereas the ventral bank
of the MCC is identified as area 24, and as homologous to the MCC in humans
95
, the
dorsal bank is identified as an extension of area F6 caudally or area 9 rostrally (Fig. 1).
Therefore, some researchers argue that the dorsal bank should not be considered part of the
cingulate cortex proper
95
, but others disagree
96
,
97
. In humans, recordings from the MCC
are typically pooled across both the dorsal bank and the ventral bank, with no attempts at
analysing the two separately. In macaques, recordings have been done mainly in the dorsal
bank
20
. Hence, uncertainty persists about whether the dorsal and ventral banks of the MCC
contribute differentially to performance monitoring, and where the boundary is between the
dorsal MCC and area F6.
Tasks for performance monitoring
Relating findings across species and neural recording modalities entails understanding
the cognitive constructs and demands of different tasks. Establishing equivalences across
species, tasks and methods remains a considerable challenge. The validity of such
comparisons depends greatly on the details of the tasks used and the instructions given.
Guided by models of cognitive control and the Research Domain Criteria framework
7
,
we consider a set of cognitive constructs that jointly allow an animal to monitor its own
behaviour. Different tasks engage these constructs to different degrees (Table 2). Although
performance monitoring itself is only one of the eight constructs considered, the other
seven constructs are all essential components needed for performance monitoring to be
possible. The constructs we consider are as follows: goal maintenance, representing and
implementing the instructed goal in a form of working memory
98
; response inhibition,
inhibiting or delaying a motor response; stimulus selection, selecting one of several possible
stimuli while ignoring others, which often requires feature or spatial attention; response
selection, selecting one of several possible motor responses, which can entail response
conflict; performance monitoring, sensitivity to consequences after an action and difficulty
during a decision; timekeeping, estimating when an event will occur or reproducing a time
interval; stimulus–response mapping, rule specifying which response should be produced
in response to which stimulus, which can be more compatible and automatic or more
incompatible and challenging; and post-error adjustments, change of performance, typically
by delaying responses, to increase accuracy
99
.
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Unlike experiments in humans, in which participants can verbalize instructions and choices,
experiments with macaques must rely on sensory–motor paradigms in which performance
is shaped through operant conditioning. In these tasks, participants express choices through
an overt action, typically an eye, forelimb or digit movement. Thus, the use of common
sensory–motor tasks can bridge the empirical gap between species.
The stop-signal (and change-signal) task examines response inhibition
100
(Fig. 4a,b and
Supplementary Fig. 1a,b). After participants adopt a posture of readiness (for example,
fixating), a stimulus is presented that requires an immediate response (‘go’ response; for
example, a gaze shift). In some proportion of trials, while the prepotent response is being
prepared, a second stimulus (the stop signal) instructs inhibition of the response, which
initiates the inhibitory ‘stop’ processes. In the change-signal version, the second stimulus
specifies an alternative response. The delay before the stop signal or change signal is
presented after the first stimulus (‘stop-signal delay’ or ‘change-signal delay’) is adjusted
trial-by-trial by a staircase procedure to allow successful responses on a subset of trials.
Performance in the stop-signal task is modelled as the outcome of a race between go and
stop processes
101
. Since the go and stop processes lead to incompatible outcomes, the
activation of both of these processes results in response conflict
102
. Within this framework,
the probability of making an error can be inferred from the stop-signal delay, which serves
as a proxy for response conflict for successfully cancelling a trial
26
,
69
,
78
. A core feature of
the stop-signal (change-signal) task is that, on every trial, there is uncertainty about whether
the stop (change) instruction will happen
103
. Because the stop-signal (change-signal) delay
is chosen randomly from a certain distribution, participants can develop predictions about
the duration of this delay
78
. Hence, another key aspect of these tasks is timekeeping
78
,
104
.
Another timekeeping demand involves maintaining the posture of inhibition (continued
fixation of the central stimulus or holding digit or forelimb posture) for a sufficient interval,
in order for performance to be qualified as ‘correct’. Thus, two kinds of error are possible.
The most common type of error is the failure of inhibition through production of the
prepotent response despite the stop (change) signal. This type of error, especially in the
case of long stop-signal delays, can result from the fact that there is not enough time for
the stopping process to finish, even if it is promptly initiated. In addition, participants can
determine that a ‘go’ response is ‘correct’ only by the absence of the stop signal. The less
common type of error is the failure to maintain the posture of inhibition (or execution of the
changed response). One key advantage of the stop-signal tasks is that the causes of errors
are relatively homogeneous, as mentioned earlier, and are well described by mathematical
models
105
. Error rates are well controlled (~50% of stop-signal trials) by adjusting the
stop-signal delay using adaptive staircase procedures. A key signature of cognitive control
is post-error slowing. In the stop-signal task, post-error slowing is prominent and involves
delaying the initiation of a go response after a non-cancelled trial
106
.
Go-no-go and anti-saccade tasks are also used to study response inhibition
107
(Supplementary Fig. 1c-f). Unlike the stop-signal (change-signal) task, however, the cue that
determines the type of trial and stimulus–response mapping rule is usually presented before
the cue instructing response initiation. Therefore, when the imperative stimulus is presented,
there is no uncertainty about the expected response. In the go–no-go task, go trials involve
a simple response (gaze shift or button press) usually with direct spatial mapping of the
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stimulus and the response. On no-go trials, participants must maintain the original posture.
In the anti-saccade task, the pro-saccade trials involve a simple orienting response with
direct spatial mapping to a visual target. The anti-saccade trials involve inhibition of the
reflexive pro-saccade and production of a saccade in the opposite direction. Performance
in the anti-saccade task differs according to whether the pro-saccade and anti-saccade trials
are intermixed or blocked, with intermixed trials resulting in a higher error rate and longer
response time
108
. Errors made in either the go–no-go task or the anti-saccade task are due
to a failure in incorporating the stimulus–response mapping rule and/or a failure in response
inhibition
107
. Of note, participants can make an error by failing to maintain the effector
position for a predetermined duration before the trial is considered successful in no-go trials
in the go–no-go task.
The Simon task, the Stroop task, the flanker task and the multisource interference task
are used to examine response selection, response inhibition, stimulus–response mapping
and performance monitoring in humans (Fig. 4c-g). In these tasks, a single visual stimulus
encodes multiple feature dimensions (spatial location, colour and/or word meaning), each
of which can prompt a different response. Participants are instructed to respond as quickly
as possible to one of the feature dimensions while ignoring the others. This is difficult
when the feature dimension to be ignored is mapped to a prepotent, even habitual response.
The source of the prepotent response is spatial (Simon task)
109
, reading (Stroop task)
110
,
irrelevant visual distractors that attract attention (flanker task)
111
or mixtures thereof
(multisource interference task)
112
. Trials on which the distractor and target dimensions
point to the same response (congruent) engender a minimal level of conflict. A mismatch
between the two dimensions (incongruent) engenders response conflict between the target
and distractor action representations. Selecting the response associated with the relevant
dimension requires inhibiting the prepotent response
75
,
113
, which leads to correct responses
with longer response times. Errors in these tasks arise endogenously: participants always
have all the information needed to determine the correct response. For the same reasons,
errors can be avoided by trading speed for accuracy. Given this property, error rates in these
tasks are more difficult to control and generally lower. All four tasks result in post-error
slowing.
Errors in the Stroop task and the multisource interference task are due to interference
between high-level cognitive processes (related to language; for example, reading a word
or number) only available in humans. Nevertheless, Stroop-like effects can be produced
in macaques using either extensive training on a particular stimulus–response mapping or
through numerosity-based competition
114
-
116
. The extensive training required to produce
these effects in macaques highlights one of the key strengths of Stroop-like tasks in humans;
that is, they produce interference and errors immediately, showing that the underlying
mechanisms do not require training.
Proposed conceptual model
We adopt the framework of forward–inverse modelling to conceptualize the processes
underlying performance monitoring and cognitive control
117
,
118
(Fig. 5) and to explain
the computation and roles of different types of ex post performance-monitoring signals. In
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motor control, the forward model estimates the state of the motor effectors (for example,
position, velocity and acceleration) on the basis of delayed and noisy sensory feedback
and predicts their future states given a motor command
119
,
120
. The inverse model generates
a motor command in a feedforward manner given a desired movement trajectory and the
effector’s current state
119
,
120
. Here we generalize these concepts to the network-level neural
dynamics involved in cognitive control and performance monitoring to propose a mechanism
by which ex post performance-monitoring signals are computed.
Viewed within the actor–critic framework
121
,
122
, the critic comprises the action and control
forward models, whereas the actor comprises the feedback controllers, control inverse
models and action selection mechanisms (Fig. 5). Each of the different actions competing
for final motor output (whether compatible or incompatible with the goal) is represented
by a corresponding action forward model (Fig. 5). After an action is selected, it is sent for
execution as a motor command and back to the critic as a corollary discharge. To compute
whether an action error occurred, this corollary discharge is compared against the output of
the forward model that predicts the goal-compatible action. To compute the ex post conflict
signal, the same corollary discharge is compared with the output of the forward model that
predicts the goal-incompatible action. The control forward model learns to predict whether
the current control settings will result in action errors and/or ex post conflict, on the basis
of task representations (possible actions) and a corollary discharge of the ‘control command’
generated by the controllers. A control prediction error is generated when either an action
error or ex post conflict occurs unexpectedly, signalling that the current control settings are
inappropriate or inadequate for the current task.
We characterize the control command in terms of the identity and intensity of the
mechanisms used to influence action selection. It is composed of feedback control generated
by feedback controllers and feedforward control generated by the ‘control inverse model’
(Fig. 5). Response inhibition triggered by the stop signal is an example of within-trial
feedback control, whereas post-error slowing
123
,
124
, post-conflict slowing
125
and conflict
sequence effects
75
,
126
are examples of between-trial feedback control. We posit that
feedback control involves global adjustments of motor readiness, arousal and attention that
influence subsequent task performance only transiently and non-specifically. The inverse
model generates the proactive control signal on the basis of the desired control outcome
(for example, a point on the speed–accuracy curve) and its knowledge of the dynamics of
the selection process and the statistical structures of the identity and intensity of control
demand in a task. An important type of control that can be influenced by either control
process is adjusting the time used for stimulus evaluation and action selection. We posit
that this form of control is subserved by time estimation processes in the MFC
127
,
128
and
response inhibition through the hyperdirect pathway (a monosynaptic axonal connection
between cortical areas and parts of the subthalamic nucleus)
103
,
129
-
132
. For a new task, a
control inverse model is initially not available, and the reactive feedback controller supplies
the entire control command. As the control inverse model is adapting, the output of the
feedback controller is gradually reduced as training proceeds. This distinction between
proactive control and reactive control is motivated by the dual mechanism of the control
framework
133
; our novel contribution is to assign these two processes to the control inverse
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model and feedback controllers, respectively, and to propose that the output from the
feedback controllers serves as a training signal for the control inverse models.
Internal models in the MFC
We next consider how the outlined model might be instantiated across different parts of the
brain (Fig. 6a). We propose that the SFG first computes action error signals and ex post
conflict signals, and then conveys them to the MCC (Fig. 6a). These ex post signals are used
to recruit feedback control, which in turn trains the control inverse model in the MCC. When
the SFG error signals occur more often, or with more intensity than predicted by the MCC,
the MCC uses the control prediction error to revise control forward and inverse models. We
posit that action selection is accomplished in the basal ganglia, with the outcome reported
back to the SFG and MCC as a corollary discharge through the thalamus.
We distinguish between the specification and implementation of cognitive control, with
the MFC being concerned primarily with the former
134
,
135
. We propose that the MCC
instantiates the control forward model (which computes control prediction error), whereas
the SFG instantiates the action forward models (which compute action error and ex post
conflict). Although many of these predictions remain to be tested, these hypotheses are
motivated by the following findings, which supplement those discussed earlier herein.
Evidence for inverse models and feedback controllers comes from findings indicating that
the MFC encodes variables that reflect aspects of the currently active control settings
18
. In
humans, MFC neurons encode conflict probability estimated from the history of experienced
conflict
18
. When two types of conflict co-occur in the same task, the probability for each
type is represented separately, suggesting that these neurons are not a reflection of generic
arousal signals. The conflict probability encoded in the MFC predicts reaction time and
error likelihood, and it explains significantly more variance in reaction times than conflict
in the immediately preceding trial alone (a proxy for feedback control). The activity of
neurons encoding conflict probability changes from one trial to the next in proportion to
the degree of conflict experienced on the current trial; we view this form of updating
as evidence for acquisition of the control inverse model
18
. A similar conflict probability
signal in the MFC can also be decoded using fMRI
136
,
137
. Similarly, the activity of
macaque SEF neurons before trial onset predicts whether animals are emphasizing speed
or accuracy in a visual search task, which is evidence for proactive control settings
122
.
The strength of the correlation between iERN amplitude and error neuron firing rate in
the MCC predicts the extent of post-error slowing, thereby providing evidence that the
MCC is involved in specifying feedback control
12
. Further causal evidence for the SFG’s
involvement in specifying or implementing control comes from microstimulation studies.
Electrical microstimulation of the SEF increases performance accuracy in the stop-signal
task by delaying saccade reaction times but does not delay saccades in simple visually
guided saccade tasks
106
. Microstimulation of the pre-SMA in a cued-switching saccade
task increases performance accuracy after response rule switching by delaying saccades
138
,
an effect possibly mediated through the hyperdirect pathway of the pre-SMA to the
subthalamic nucleus
139
. Thus, experimental evidence indicates that the MFC plays a role
in implementing the inverse models and the feedback controller.
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Evidence for forward models comes from examining representations of choices and ex
post conflict signals. First, during a task in which participants make either a perceptual
or a memory-based ‘yes’ versus ‘no’ decision, neurons with a stimulus onset-triggered
response in the pre-SMA predicted the subsequent choice regardless of whether it was
true or false
34
. This encoding of choice is task dependent but effector (button press or
saccade) independent, suggesting that these neurons represent a mapping between the
stimulus/internal representation and the ‘goal-appropriate’ response based on the current
task rule (Fig. 5). Similarly, in a study of value-based decision making, pre-SMA neurons
also predicted choices well before action execution
140
. These data indicate that during action
selection, the SFG represents target and distractor responses instead of actual movement (for
example, which button is pressed) as expected from a forward model. Second, in macaques
performing the stop-signal task, neurons in the SFG that encode ex post conflict signals are
defined by their correlation with the probability of non-cancelled saccades
69
,
77
,
78
. This is
compatible with representations of forward models because before the stop signal occurs,
the action forward model representing the ‘go’ action makes a prediction that a saccade
motor plan will be selected for output, with its probability increasing as a function of time.
The longer the stop-signal delay, the greater the probability is for the ‘go’ saccade to occur
and the larger the prediction error is when the ‘go’ action is successfully cancelled, thereby
resulting in an ex post conflict signal
102
.
Lastly, we consider the distinct and common roles of the SFG and the MCC. In both humans
and macaques, error signals appear first in the SFG and then in the MCC. This indicates
a hierarchical relationship: the MCC learns to predict the internal error signals computed
by the SFG to adjust control settings for future trials
141
. An unexpected action error can
signal insufficient control settings, which necessitates the recruitment of feedback control,
or incorrect control settings (for example, a change of task or reward environment), which
necessitates the revision of control forward models. In macaques, simultaneous recordings
in the MCC and the SFG during a time estimation task, in which errors are informed by a
loss of reward, provide support for this conclusion
141
. Although error signals were present
in both areas, only baseline activity in the MCC showed signatures of a control inverse
model by predicting the animals’ switching to an alternative response rule. In the same
task, electrical microstimulation in the SFG increased activity in the MCC, but only when
the appropriate response rule was inferred rather than instructed. These results support the
view that directional flow from the SFG to the MCC is a key aspect of bridging monitoring
with control, with the SFG implementing the forward model and the MCC implementing
the inverse model. We note that the purpose of the proposed model is to explain only error
detection instead of the many other functions of the MFC
14
,
17
,
38
-
40
.
Implementing action error computations
We envision that the computational processes that underlie action errors interact with other
parts of the brain (Fig. 6). The action selection process is a biased competition that occurs
in cortico–basal ganglia–thalamocortical loops
142
-
145
and is influenced by top-down inputs
that originate in the dorsolateral prefrontal cortex and represent behavioural goals
142
. The
conclusion of this competition or selection process produces a motor output, together with
a corollary discharge that conveys the action choice back to the SFG (as well as other parts
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of the cortex, including the MCC
146
; but note that the thalamic nuclei that project to the
SFG are different from those that project to the MCC) via projections from the thalamus
to deep layer 3. This is based on the finding that the neurons at the layer 3–layer 5 border
signal errors earlier than do others
13
. There, the actual action choice is compared with the
predicted action choice generated by the action forward models. On the basis of whether
these two signals match or do not match, an error signal is generated, which then propagates
to layer 2 and layer 6, conveyed to various upstream and downstream targets (Fig. 6a),
such as the MCC. Although most of the experimental evidence supporting our model is
obtained directly from agranular SEF data
147
,
148
, some of the assumptions of the underlying
microcircuitry are based on work on granular neocortex
149
and remain to be confirmed for
agranular cortex.
The experimental findings discussed above inform the following potential circuit-level
implementation. In the SFG, the predicted action choice and the actual action choice
(thalamic inputs) are represented by multivariate patterns of neural activity, and the
matching of these patterns may provide a mechanism for detecting errors. Type I neurons
may implement anti-coincidence detection (active when there is a mismatch and inactive
when there is a match or no inputs; that is, an exclusive or (XOR) operation), whereas type
II neurons implement coincidence detection
150
(active when there is a match and inactive
when there is a mismatch or no input; that is, an AND operation). These two processes
might be implemented biophysically at the single-neuron level through a combination of
strong after hyperpolarization and/or nonlinear dendritic computations
151
, with the two
types of neuron competing for activation through a soft winner-takes-all process mediated by
shared inhibition
152
,
153
(Fig. 6c).
Across the cortical sheet of the MFC, dipoles created by synchronous thalamic inputs
and the resultant returning current at the apical dendrites, as well as top-down inhibition
mediated by intralaminar inhibitory neurons, are aligned, and summate to generate the
ERN (Fig. 1b). These dipoles are possibly strengthened through the activation of calcium
spikes initiated in the layer 5 pyramidal neurons
55
(also see ref.
48
). In humans, most error
neurons are of type I (74% in the MCC and 60% in the pre-SMA), indicating that they may
contribute more to the dipoles that give rise to the ERN than type II error neurons. However,
the relationships between the scalp ERN, the iERN measured in different parts of the MFC
and single-neuron activity are complex and little understood (similarly for BOLD signals),
leaving it an open question to what extent type I and type II error neurons (and the resulting
iERN) differentially contribute to these macroscale metrics of error monitoring.
If error computations are subserved by specific cortical microcircuitry, we would expect
that error neurons compute errors across different tasks. In support of this, in the human
MFC, most error neurons are domain general, with the others being dependent on the task
performed
18
. Alternatively, error neurons might emerge from learning of specific tasks.
Further studies are needed to investigate how error signalling changes as a function of
learning several tasks to investigate this prediction.
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Predictions and suggested experiments
In our model, action errors are computed on the basis of a corollary discharge conveying
the action choice rather than the proprioceptive feedback generated by executing the action
to the MFC (this hypothesis is mentioned in one of the first articles describing the ERN
46
).
We argue that the corollary discharge is provided through thalamic input to layer 3/5 of
the SFG. This assumption predicts that disrupting thalamic input will disrupt action error
computation but disrupting proprioceptive feedback will not. In support of this, the ERN is
abolished and error detection is compromised in patients with lesions in the ventral anterior
nucleus and the ventral lateral anterior nucleus of the thalamus
154
. In primates, both the
MCC
146
,
155
and the SFG
156
are innervated by neurons in these nuclei of the motor thalamus.
Therefore, lesions in the ventral anterior nucleus or the ventral lateral anterior nucleus are
expected to compromise the corollary discharge to both the MCC and the SFG, leading to
profound deficits in error signalling, which is demonstrated in this lesion study
154
. Transient
inhibition of the ventral anterior nucleus and the ventral lateral anterior nucleus results in
increased error rates in inhibiting saccades
157
. This suggests that these nuclei are important
for response competition and thus are potential sources of the corollary discharge that
reports the results of this competition. The SFG, in addition, is innervated strongly by
neurons in the mediodorsal nucleus. Consistent with this, the ERN in an anti-saccade task
is attenuated by lesions to this nucleus
158
. By contrast, the ERN was largely normal in a
deafferented patient without sensory feedback
159
. A critical experiment will be to examine
the effects of transiently disrupting neuronal activity in these nuclei on the activity of error
neurons. A case study of a patient with an extensive unilateral MFC lesion provided direct
behavioural support for a role of a corollary discharge in learning from errors. In that study,
learning from external feedback was impaired only when the individual was not able to
observe the action he had executed, in which case visual feedback was not available and
the patient thus had to rely on the corollary discharge to attribute outcomes to a specific
action
160
.
Lesioning the SFG (but not the MCC) should impair action forward models, resulting
in a loss of action error and ex post conflict signals. As a result, controllers cannot be
recruited to ensure the achievement of goals, and behaviours are dominated by habitual
actions
161
. Lesioning the MCC, by contrast, would not abolish error detection but would
rather compromise the ability to use the error signals generated by the SFG to acquire
control forward and inverse models. As a result, such lesions would result in an over-reliance
on feedback control, a failure to engage proactive control and a failure to learn from internal
error signals.
Action errors are different from the types of ‘action outcome’ that may serve to update
internal models of the external environment rather than of cognitive control. We therefore
predict that action errors can recruit feedback control but exogenously signalled action
outcomes do not. Results from a human behavioural study of fast typing provide support
for this prediction
2
. A proportion of action errors made by typists were covertly corrected,
whereas some correctly typed contents were covertly switched to typographical errors.
Although the typists took ownership of both the corrected errors and the inserted errors and
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thus subjectively experienced agency for both (thus qualified as ‘action outcomes’), only the
corrected errors triggered post-error slowing.
We expect that the MCC signals estimates of the ‘volatility’ of the rates of commiting action
error or experiencing response conflict. Volatility is defined here as the rate of change over
time
137
,
162
. The stop-signal task is well suited to test this as the target error rate can be
externally controlled. In this task, in a high-volatility block, the target error rate would vary
frequently. Similarly, in a response conflict task, in a high-volatility condition the probability
that a given trial contains conflict would vary frequently. We expect that the error signals in
the MCC depend on volatility, whereas those in the SFG do not. This experiment can thus
dissociate action error signals from control prediction error signals: the former should not
depend on volatility manipulations, whereas the latter should.
We posit that action errors are represented separately from reward prediction errors, and
that it is the cells that signal action errors but not those signalling reward prediction errors
that predict the amplitude of the ERN. This prediction is borne out in the macaque SEF,
where error neurons are distinct from neurons signalling gain or loss of rewards and the
ERN amplitude is predicted by the former, but not the latter
13
. In addition, in a difficult
visual search task that dissociates choice errors from reward prediction errors, distinct
neurons in the SEF signalled each error type
122
. In the macaque pre-SMA and SMA, most
error neurons do not respond to unexpected reward
16
. By contrast, many (not all) of the
action error neurons in the macaque MCC also signalled unexpected omission of rewards
19
,
suggesting that the MCC error neurons are more abstract
163
, and that the SFG action error
signals may represent one of the many inputs to these neurons. This prediction remains to be
tested in humans.
As an action error predicts loss of reward, it must be conveyed to the midbrain dopaminergic
system to serve as an input for computing reward prediction error. We expect simultaneous
recordings from MFC error neurons and midbrain dopaminergic neurons to show that the
responses of these two groups of neuron are correlated on a trial-to-trial basis in terms
of magnitude and latency, that MFC error neurons respond earlier than dopaminergic error-
signalling neurons and that disrupting error signals in the MFC abolishes error signals in
dopaminergic neurons. This proposal is backed by established evidence that dopaminergic
neurons integrate cortical inputs
164
. By contrast, if dopaminergic error neurons fire earlier
than MFC error neurons, the reinforcement learning (RL)–ERN model
48
, which posits that
action errors are merely prediction errors, would be supported (see the discussion later for
details of this model and further justification of this experiment).
It is important to establish the flow of error information from the SFG to the MCC. In
the MCC, we predict that feedforward input from the SFG provides error signals to the
superficial layers, which would be visible as superficial sinks in laminar recordings and
earlier responses by layer 2/3 error cells relative to those in deeper layers. Disrupting action
error signalling in the SFG should abolish these sinks and sources in the MCC as well as
error neuron firing in the MCC.
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We hypothesize that the MCC instantiates the control inverse models, whereas the SFG
instantiates the feedback controller. This proposal predicts that disrupting MCC function
will disrupt the acquisition of control inverse models, thereby increasing reliance on
the feedback controller, whereas disrupting SFG ex post signalling will disrupt both the
feedback controller and the control inverse model. The magnitude of the ex ante conflict
signal would be expected to track such causal manipulations. A well-learned control
inverse model (for a particular task) could generate the feedforward control commands that
anticipate conflict, effectively minimizing the conflict experienced during action selection.
Consistent with this, prolonged practice greatly diminishes the Stroop effect
110
. We predict
that the responses of neurons signalling ex ante conflict in the MFC become weaker as
participants gain more practice in a task involving response conflict. Compatible with this
interpretation, a human neuroimaging study
165
showed that conflict signals were greatly
diminished when conflict was predicted by a preceding visual cue.
The control inverse model should allow participants to proactively switch between multiple
sets of control settings. One setup for testing different control settings is to change
the speed–accuracy trade-off by instruction, as we did recently in macaques
122
,
166
. The
expectation would be that MFC neurons accomplishing the control inverse model, such
as the recently described neurons signalling conflict probability
18
, change their activity
systematically following such cues. Again, we expect that ex ante conflict neurons would be
modulated on the basis of the control settings for otherwise identical stimuli.
Computing the control command requires domain-specific control signals (for example,
specific control signals targeting the specific type of conflict experienced). However, before
the control inverse model is acquired, the feedback controller generates generic controls
signal (for example, global changes in the level of arousal and/or excitation in the motor
system). Consistent with this, human MFC neurons represent both domain-general and task-
specific monitoring signals
18
. A key experiment will be to examine the temporal dynamics
of these two types of signal, with the expectation that for a new task, domain-general
signals are available immediately, whereas task-specific signals emerge gradually with task
exposure and practice.
Theoretical models indicate that the largest source of the scalp ERN is the cingulate sulcus,
owing to the orientation of the neuronal dipoles (Fig. 1b). If so, the scalp ERN should
primarily reflect error signals in the MCC, which as we describe here appear later than
those in the SFG. However, in hemispheres with a PCS or at more caudal locations, dipoles
within the SFG are well positioned to impact the scalp ERN as well. This is compatible with
the findings we reviewed that activity in the SFG is predictive of the scalp ERN amplitude
and latency. It will be important to determine experimentally the extent to which dipoles
in different parts of the MFC differentially contribute to the scalp ERN using simultaneous
intracranial recordings. In addition, a critical open question is how performance-monitoring
signals other than action errors, such as ex post conflict and control prediction errors,
influence the scalp ERN.
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